Model-Based Object Recognition from a Complex Binary Imagery Using Genetic Algorithm

This Paper describes a technique for model-based Object recognition in a noisy and cluttered environment, by extending the work presented in an earlier study by the authors. In Order to accurately model small irregularly shaped objects, the model and the image are represented by their binary edge maps, rather then approximating them with straight line Segments. The Problem is then formulated as that of finding the best describing match between a hypothesized Object and the image. A special form of template matthing is used to deal with the noisy environment, where the templates are generated on-line by a Genetic Algorithm. For experiments, two complex test images have been considered and the results when compared with Standard techniques indicate the scope for further research in this direction.

[1]  William Grimson,et al.  Object recognition by computer - the role of geometric constraints , 1991 .

[2]  Azriel Rosenfeld,et al.  Using probabilistic domain knowledge to reduce the expected computational cost of template matching , 1990, Comput. Vis. Graph. Image Process..

[3]  W. Eric L. Grimson,et al.  On the Sensitivity of the Hough Transform for Object Recognition , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Tatsuya Akutsu,et al.  Distribution of Distances and Triangles in a Point Set and Algorithms for Computing the Largest Common Point Sets , 1998, Discret. Comput. Geom..

[5]  Sivan Toledo,et al.  Applications of parametric searching in geometric optimization , 1992, SODA '92.

[6]  Martin D. Levine,et al.  Geometric Primitive Extraction Using a Genetic Algorithm , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  W. Eric L. Grimson,et al.  The effect of indexing on the complexity of object recognition , 1990, [1990] Proceedings Third International Conference on Computer Vision.

[8]  Dana H. Ballard,et al.  Computer Vision , 1982 .

[9]  Leonid P. Jaroslavskij Digital picture processing , 1985 .

[10]  Shiu Yin Yuen,et al.  Connective hough transform , 1993, Image Vis. Comput..

[11]  Zhi-Qiang Liu,et al.  Multiobject pattern recognition and detection in noisy backgrounds using a hierarchical approach , 1988, Computer Vision Graphics and Image Processing.

[12]  Daniel P. Huttenlocher,et al.  Comparing Images Using the Hausdorff Distance , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Chris Loader Local Search Algorithms for 2D Geometric Object Recognition , 1995 .

[14]  Micha Sharir,et al.  The upper envelope of voronoi surfaces and its applications , 1993, Discret. Comput. Geom..

[15]  J. Beveridge Local search algorithms for geometric object recognition: optimal correspondence and pose , 1993 .

[16]  Karen B. Sarachik Limitations of Geometric Hashing in the Presence of Gaussian Noise , 1992 .

[17]  Juyang Weng,et al.  Genetic algorithms for object recognition in a complex scene , 1995, Proceedings., International Conference on Image Processing.

[18]  Christopher J. Taylor,et al.  Model-based image interpretation using genetic algorithms , 1992, Image Vis. Comput..

[19]  Thomas Bäck,et al.  Parallel Problem Solving from Nature — PPSN V , 1998, Lecture Notes in Computer Science.

[20]  Helmut Alt,et al.  Approximate Matching of Polygonal Shapes (Extended Abstract) , 1991, SCG.

[21]  Erkki Oja,et al.  Randomized hough transform (rht) : Basic mech-anisms, algorithms, and computational complexities , 1993 .

[22]  Yehezkel Lamdan,et al.  Geometric Hashing: A General And Efficient Model-based Recognition Scheme , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[23]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[24]  Kalyanmoy Deb,et al.  Analytic Curve Detection from a Noisy Binary Edge Map Using Genetic Algorithm , 1998, PPSN.

[25]  Arthur R. Pope Model-Based Object Recognition - A Survey of Recent Research , 1994 .

[26]  William Rucklidge,et al.  Locating objects using the Hausdorff distance , 1995, Proceedings of IEEE International Conference on Computer Vision.